The rise of multimedia computing appliances and digital video transmission has led to an increased need to store and manipulate many-colored and complex digital images. Examples of many-colored and complex digital images include: digital photographic images of the natural world, whether taken with a digital camera or digitized from an analog camera print; computer-generated images of the natural world; and/or computer-generated images which include anti-aliased text or graphics.
Anti-aliased images, in particular, are increasing in both frequency of occurrence and importance in the art. This is primarily due to the increased expectations regarding visual appearance of the user interface in word processing and other text and/or graphic orientated programs. Anti-aliasing is a technique well known to those of skill in the art whereby the edges of an image are blurred or “softened” to reduce the visual artifacts produced by finite pixel sizes.
Due to the complexity of many-colored digital images, it is often necessary to compress the images in order to save storage space and/or minimize bandwidth when storing or transmitting the digital images. The compression/decompression process typically uses standardized algorithms well known to those of skill in the art. The algorithms for compressing and decompressing the images are known generically as COmpressor/DECompressors or “codecs”.
Codecs are typically grouped into two main types; lossless and lossy. Lossless codecs, like LZ coding and GIF, preserve the image information in its exact form. While providing virtually perfect image replication capabilities, lossless codecs tend to provide less compression opportunities and require more resources, such as storage space and transmission bandwidth, to employ. On the other hand, lossy codecs, such as JPEG and vector quantization, store only an approximate representation of the image. Lossy codecs are typically formulated based on the capabilities and limitations of the human visual system to detect subtle differences in color. In other words, granularity and detail beyond that capable of being detected by the human eye are disregarded and do not survive the compression process. Since, using lossy codecs, digital information is selectively discarded, lossy codecs typically achieve much better compression than lossless codecs while still maintaining acceptable quality.
In addition to the true multi-colored images needing to be transmitted, many anti-aliased images of only two or three base colors are also treated as many colored images by traditional encoding mechanisms. This is because while many anti-aliased images, such as those resulting from anti-aliasing two or three color text images or designs, have a low inherent information content, i.e., two or three base colors with shading variations thereof, the images appear to traditional encoding mechanisms as many colored images with a complexity similar to that of a natural image.
For instance, an anti-aliased text image may contain base colors of black and white, with all other color variations being shades of gray. Consequently, during compression, all that is needed is the information that all pixel values are a shade of gray, and the shade value for each pixel. However, in the prior art, each pixel was treated as a new color being one of 256^3 possibilities, requiring three bytes of data per pixel. Consequently, in the prior art, for a given degree of lossiness, the encoder often obtained the same, or even worse, compression for an anti-aliased image as it would for a true many color natural image.
What is needed is a method of identifying anti aliased images so the anti-aliased images can be more efficiently compressed than was possible using prior art methods and encoders.